Data Mining

Describe the steps involved in data mining when viewed as a process of knowledge discovery.

Compare ROLAP, MOLAP, OLAP operations. Explain different OLAP operations.

Explain data transformation strategies in detail.

In real world data, tuples with missing values for some attributes are a common occurrence. Describe various methods for handling this problem.

Draw a snowflake schema diagram for "University" data warehouse which consist of four dimensions student, course, semester and instructor and two measures count and avg_grade. At the lowest conceptual level, the avg_grade measure stores the actual course grade of student. At higher level avg_grades store average grade for given schema.

Give an example to show that items in a strong association rule actually may be negatively correlated.

Describe knowledge discovery process in detail.

Apriori algorithm makes use of prior knowledge of subset support properties. Prove that all non empty subsets of a frequent itemset must also be frequent.

Why is tree pruning useful in decision tree induction? What is a drawback of using a separate set of tuples to evaluate pruning?

Give an example to show why the k-means algorithm may not find the global optimum, that is optimizing the within cluster variation.

Clusters may form a hierarchy, outliers may belong to different granularity level. Explain a clustering based outlier detection method that can find outliers at different levels.

What is Web mining? Explain its types.

Describe the security issues in data mining.

Describe Spatial and Temporal mining with suitable example.

Write K-means algorithm with example.